Based On Morphological Image Segmentation Applications

Segmentation is an important image technology, it has not only been widespread attention and research, also received a large number of practical applications. In image research and application, people usually interested in some region of image, which is often called target or foreground (the others is called background).It generally indicates the specific region with special property in the image. In order to identify and analyze the target in the image, separation of the region from the image is needed. Based on which, people can measure the target and utilize the image more. Segmentation refers to the image into different regions and extracted the target of interest to the technology and process. The image segmentation is difficult to target the region's low-level information have often inconsistent, unable to achieve the correct partition. As a result, many scholars have been committed to the method of image segmentation research, and put forward a number of well-established methods.Segmentation by mathematics morphology is main goal of this paper. So, in this paper, we introduce origin of mathematics morphology from binary morphology to gray morphology and extensively study its different operators and quality. Then, we were on the brink of the morphology-based detection and the integration of space-time video image segmentation discussed.The morphological gradient operators of edge detection with multi-structuring elements and multi-scale are given for gray-scale image and noise. Five elements are used to detect multi—directional edges of image at different scales, The more ideal image edges under the environment of existing noise are obtained by integrating the edge characteristics for various scale.In the MPEG-4 standard, video frame was composed of a series of independent semantic video object, which was encoded independently. So, the performance of the segmentation algorithm is crucial to the final MPEG-4 coding products. Video segmentation is a challenging topic of research, no single algorithm has been reported in the literature that is generally applicable. To implement the object—based video coding, a video sequence is needed to segment it into several individual objects. this paper presents an algorithm of video object segmentation based on spatial—temporal information fusion. In the time domain, the use of the adjacent frame difference method to find the initial position of moving targets in space, the use of algorithms to improve the watershed in the movement of the target location accuracy, the final results from the two projection operations, has been the ultimate goal of movement.